In modern day animal farming there is an increasing need for the automated livestock monitoring, in particular the (automated) collection of event-based behavioural responses of said animal, and the integration of these animal responses in livestock farming (Aerts et al., 2003, Biosystems Engineering 84, 257-266). In particular, computer and modern electronic technologies have been applied to monitor the behaviour, performance and welfare of farm animals.
In the last years, the analysis of farm sounds, particularly farm animal vocalisation, has gained increasing interest and a variety of attempts to decode the meaning has been made. In this context, Manteuffel et al. (2004; Applied Animal Behaviour Science 88 (1-2), 163-182) provide an overview of and problems associated with the vocalization of farm animals as a measure of animal welfare. Other approaches to evaluate and monitor animal welfare have examined the relationship between vocalisation or drinking behaviour and animal welfare (Exadaktylos et al, 2008, Comput. Electron. Agric., 63, 207-214; Madsen and Kristensen, 2005; Van Hirtum & Berckmans, 2004, Transactions of the ASAE 47, 351-356; Silva et al., 2008, Comput. Electron. Agric. 64, 286-292). In addition, research has been conducted on automation of welfare monitoring through water use estimation (Kashiha et al., 2013, Comput. Electron. Agric., 90, 164-169.), weight estimation (Brandi and Jorgensen, 1996, Comput. Electron. Agric., 15, 57-72), or locomotion analysis (Lind et al., 2005, J Neurosci Meth, 143, 123-132). Also, image processing has been used as well for monitoring the welfare of animals such as pigs (Xin, 1999, J. Anim. Sci., 77, 1-9), poultry (Sergeant et al., 1998, Comput. Electron. Agric., 21, 1-18), dairy cows, etc.
In addition to pig vocalisation, a lot of research on poultry behaviour and welfare related to sound vocalisation is presented in previous literature. The question of how management or environmental stimuli may influence poultry behaviour and/or well-being is of considerable importance for fundamental studies of behavioural response to stimuli, and as a means of assessing appropriate management and environmental designs for commercial production. One means of assessing bird response to stimuli involves careful analysis of characteristics of individuals or groups over time. Monitoring individual behaviour during research trials is typically performed with some type of video imaging system. For poultry, behavioural activities are categorized into events such as eating, drinking, preening, resting, and stereotype activities directed at different targets. This assessment methodology is time-consuming, hence costly, tedious and prone to errors, even with modern commercially available research systems that compile the statistics semi-autonomously. For this purpose, individual bird feeding statistics and stereotyped pecking behaviour from time-series recordings of feed weight were developed and compared to video observations (Gates and Xin, 2008, Comput. Electron. Agric., 62, 8-14). In another study, for turkey breeding, a structured query language (SQL) database management system was developed by Xuyong et al., 2011, Comput. Electron. Agric., 75, 313-320) to record and manage the dynamic feed intake measured by weighing scale and body weight gain data of individual birds. In other studies, animal behaviour was videotaped and chewing sound was recorded using a microphone attached to the steers' foreheads by Galli et al., 2005, Animal Feed Sci Technol, 128, 14-30) to evaluate acoustic analysis as a means to monitor and quantify chewing behaviour, and to estimate DM (dry matter) intake of forages with a wide range of water and fibre content. In another study, two methods (the IGER Behaviour Recorder (IBR) and acoustic monitoring (ACM)) for the detection and classification of jaw movements in grazing dairy cattle were compared by Ungar and Rutter, 2005, Appl Animal Behaviour Sci, 98, 11-27), wherein sound was detected by a microphone mounted against the cow's forehead with signals transmitted to a camcorder. Although these sound based methods may work with cattle, it is not possible to use it with 100,000 chickens in a broiler house.
Thus, in general, there remains a need, particularly in livestock farms, to have a reliable identification of animal nutriment (feed and water) intake, as this provides information on animal welfare and is also vital to reach a sound financial operation of the farm. Indeed, a reliable identification of feed intake is important to reach the right feed conversation rate, to calculate the waste of the food in each pen, to define the eating period and to define the dynamic feeding behaviour of the animals.